Reinforcement fine-tuning for Amazon Nova: Teaching AI through feedback

Amazon has introduced reinforcement fine-tuning (RFT) as a new paradigm for customizing its Nova AI models, shifting from learning by imitation to learning by evaluation. This technique addresses a major pain point in enterprise AI adoption: the prohibitive cost and time required to create thousands of detailed, step-by-step demonstrations for traditional supervised fine-tuning. Instead, RFT allows developers to provide prompts and define correct outcomes through test cases, verifiable results, or quality criteria. The model then learns to optimize for these criteria through iterative feedback, discovering its own solution paths. This is especially powerful for tasks with multiple valid answers, like code generation or nuanced customer service.

RFT is now available across Amazon's AI stack, from the fully-managed Amazon Bedrock to SageMaker Training Jobs and the advanced Nova Forge for multi-turn agentic workflows. The launch is strategically timed with the Nova 2 family, Amazon's first models with built-in reasoning capabilities that perform intermediate thinking steps. RFT can optimize not just the final answer but the reasoning process itself, teaching the model more efficient paths and reducing token usage. Initial supported use cases are text-only and include code generation (where correctness and efficiency can be verified programmatically), customer service tone optimization, content moderation, and complex financial or legal analysis. This represents a significant lowering of the barrier to creating highly specialized, compliant, and efficient enterprise AI applications.